Optimal graph representations and neural networks for multichannel time series data in seizure phase classification.

In recent years, several machine-learning (ML) solutions have been proposed to solve the problems of seizure detection, seizure phase classification, seizure prediction, and seizure onset zone (SOZ) localization, achieving excellent performance with accuracy levels above 95%. However, none of these solutions has been fully deployed in clinical settings. The primary reason has been a lack of trust from clinicians towards the complex decision-making operability of ML. More recently, research efforts have focused on systematized and generalizable frameworks of ML models that are clinician-friendly. In this paper, we propose a generalizable pipeline that leverages graph representation data structures as a flexible tool for graph neural networks. Moreover, we conducted an analysis of graph neural networks (GNN), a paradigm of artificial neural networks optimized to operate on graph-structured data, as a framework to classify seizure phases (preictal vs. ictal vs. postictal) from intracranial electroencephalographic (iEEG) data. We employed two multi-center international datasets, comprising 23 and 16 patients and 5 and 7 h of iEEG recordings. We evaluated four GNN models, with the highest performance achieving a seizure phase classification accuracy of 97%, demonstrating its potential for clinical application. Moreover, we show that by leveraging t-SNE, a statistical method for visualizing high-dimensional data, we can analyze how GNN's influence the iEEG and graph representation embedding space. We also discuss the scientific implications of our findings and provide insights into future research directions for enhancing the generalizability of ML models in clinical practice.